future of robo-advisors in investment and wealth management
TRANSCRIPT
Future of Robo-Advisorsin Investment and Wealth Management
Robo-advisors are digital platforms that provide
automated and algorithm-driven financial planning
services with little to no human supervision.
The concept of robo-advisors started during the
financial crisis of 2008, when small investors
had cash surplus as they pulled money out of
equities and interest rates touched zero.
Betterment, the leading robo-advisory firm,
seized this opportunity by encouraging investors
to invest in algorithm-driven portfolios for getting
steady returns. Since then, robo-advisory became
the buzzword of the wealth management industry
and many traditional players have now launched
robo-advisors as part of their solution portfolio. In
spite of the increased adoption, robo-advisory is
yet to match up to the hype around its origin, and
robo-advised assets are a drop in the ocean at
$1.4 Trillion in Jan 2020 in comparison to the size
of the investible assets at $22 Trillion and overall
$9 Trillion cash sitting on the sidelines.
Robo-advisors are designed to understand investor
needs, propose the investment and allocation
strategy, implement the selected allocations,
monitor results, and perform the portfolio
rebalancing as per the strategy. Robo-advisors
offer easy account setup, robust goal planning,
account services, portfolio management, security
features, enhanced customer service, and
comprehensive education. In addition, services
such as tax-loss harvesting and retirement
planning, traditionally offered only by large
wealth managers, are often packaged into the
robo-advisory offering. Robo-advisors provide
efficiencies in addressing a wider range of investors
including underpenetrated mass affluent and small
investor segments of wealth management, and
differentiate through intuitive investing experience
with fully digital and online processes.
The challenge with robo-advisory lies partly in the
design of the platforms, which limit personalization
for self-driven investors while access to skilled
wealth advisors is often a premium solution.
Robo-advice is yet untested in volatile markets
and their performance during deep disruption akin
to the COVID-19 situation will be closely watched.
Roboadvisors:Pros and
Cons
Addressing large underserved market
with low fees and no minimum
account balance requirement making
it affordable for Millennials and
emerging Gen workers
Simplicity and transparency
through unbiased offerings, and
providing a detailed level of
reporting to conform to industry
wide rules and regulations
Convenience and accessibility through
digital application and interacting
tools, with real time dashboards and
alerts to the customer to take
required actions
Lack Personalization as financial advice
providers, but may look into further enhancing
the user experience through advanced
robotics concepts like NLP and deep learning
to bring in more personalization in their
interaction and operations
Keeping the algorithm up-to-date with
changing environment, need for more robust
modelling along with effective ways of making
decision engine upgrades with quick
turnaround time.
Cannot handhold investors during downturns
as can the traditional model with human
intervention, robo-advisors as of now have
been more effective under relatively stable or
bullish market conditions
Pros Cons
Fully Integrated Robo-Advisor
The robo-advisor is integrated with the
bank’s business model and clients of robo-
advisor are clients of the bank. It is neither
an independent advisor nor a separate legal
entity and does not exist outside the bank’s
service offering. Eg. Schwab Intelligent
Portfolios (AUM: $47.2B) and Vanguard
Personal Advisory Service (AUM: $115.5B)
Stand-alone Robo-Advisor
They are not influenced by any specific
product in market. They are not allowed to
take any inducement This model allows to
provide independent advice according to
MiFID II. Eg, Wealthfront (AUM: $13.5B),
Betterment (AUM: $15.6B)
Segregated Robo-Advisor
Robo-advisory is a segregated offering and
may function independently with the parent
or jointly to provide chosen offerings
Robo for Advisor
Targeted for Wealth & Asset Management
advisors. Provides the human advisor touch
and creates a distribution channel for Robo
advisors. Eg. Betterment for Advisor
Fig: Different Robo-advisory business models
Current State of Robo-Advisory
1.1 Robo-advisory business models
Robo advisory firms broadly operate under
four types of business models as shown below.
1 2
43
Robo-advisors are trying to capture various
segments through different business models and
customized solutions for investors and incumbent
asset and wealth management firms that leverage
these robo-advisory platforms to build their digital
advice capabilities. From being standalone robo
advisors, many firms have expanded through
integration with other financial institutions, adding
more products like Retirement and CDs, and struck
partnerships with RIAs (Registered Investment
advisors) for providing investment advice.
The difference is based on how they interact with
the customers and integrate within the banking
parent to provide to provide allied services and
distribution models.
• Assets/customer : < $250,000
• May not have required
financial knowledge of
making investing decisions
• Limited account balance
and cannot afford the
professional financial
advice
• Have a simple portfolio
and able to manage their
portfolio online with
limited/no human
assistance
• Assets/customer : $250,000 - $1 M
• Can afford professional
financial advice
• Have mix of basic &
complex needs, looking for
newer ways of investing
• Will need human
involvement for complex
decisions but satisfied
with technological support
for basic needs
• Assets/customer: HNI: $ 1-100 M; UHNI: >$100M
• Affordability is not an
issue for them
• Have very complex needs:
have to manage multiple
assets, constant change in
portfolio, frequent
rebalancing
• To fulfil their needs, wealth
managers need robust
models and technological
support to provide
recommendations
Persona
Suitable RoboModel
RoboAdvisory Adoption
Young InvestorMass Affluent
Affluent Investor
HNI & UNHI Investors
• Standalone Robo Advisor
• Segregated Robo Advisor
• Fully Integrated Robo
Advisor
• Standalone Robo Advisor
• Segregated Robo Advisor
• Fully Integrated Robo
Advisor
• Robo for Advisor
• Fully Integrated Robo
Advisor
Fig: Different Robo-advisory business models
Features
1.2 Persona Mapping
Based on the personal assets of an individual,
customers of the wealth management industry
are divided into 4 segments: Retail/ Mass affluent
(Assets: <$250,000), Affluent (Assets: $250,000 -
$1M), HNI (Assets: $1-100M) and UHNI (Assets:
>$100M). Per 2018 statistics, 34% of global
personal wealth belonged to Retail/mass affluent,
16% belonged to affluent customers, 38% to HNIs
and 12% to UHNIs.
The persona mapping for each customer segment
based on their needs and suitable robo-advisory
model is shown below:
On an average, for a wealth manager, retail and
affluent segments hold 17% of the AUM but
generate 27% of the revenues. They have a
higher revenue per unit asset as compared to
the HNI/UHNI but at the same time, revenue per
customer is much less. Hence, this segment is
neglected with no dedicated business unit or
helpdesk serving it. As the robo-advisory model
becomes more sophisticated, it will be able to
serve the needs of this retail and affluent segment,
and can increase revenue per customer with better
control over operational costs. Hence, the scope of
adoption of robo-advisors for retail and affluent
segments is expected to be much higher as
compared to HNI and UHNI.
1.3 Regulations governing Robo-Advisor
Regulators around the world have suggested that
the registration requirements for investment
selection, investment recommendations, and asset
allocation are technology-neutral. This means that
the registration, know your customer (KYC), and
information requirements of traditional wealth
managers will apply to digital / robo-advisors.
Therefore, robo-advisors must register with the
Securities and Exchange Commission of a given
country to conduct business and are subject to
similar securities laws and regulations as
traditional broker-dealers.
So far, there is no specific regulatory framework
related to robo-advisory except to the Joint
Committee Discussion Paper on Automation in
financial advice of ESMA, EBA, and EIOPA. However,
there are different regulations that are not directly
related to robo-advisory that can be entirely or
partially applicable. Some of the regulations from
leading markets for robo-advisory are
covered below –
• On June 5, 2019, the US Securities and Exchange
Commission (SEC) adopted new regulation
“Regulations Best Interest” that imposes
principles-based standards on broker-dealers,
and requires a broker-dealer to act in its retail
customer’s best interest when making such
recommendations. To meet this “best interest”
standard, broker-dealers must satisfy a number
of requirements and specific obligations related
to disclosure, standard of care, conflicts of
interest, and compliance.
• The SEC, through the Office of Compliance
Inspections and Examinations (OCIE), issued its
annual Examination Priorities for 2019, along with
five Risk Alerts for consideration and action over
the course of the year. The annual Examination
Priorities publication highlights many “perennial
risk areas” including data security, integrity,
compliance, and transparency. This serves as a
useful guide for newer investment management
firms, as well as those that have never been
examined by the commission.
• As per the rules defined by MiFID and MiFID II,
two main issues to be considered in the digital
advisory services is how to comply with Suitability
and Appropriateness, and how to deal with
Privacy rules. Robo-Advisors should implement
best practices to control and monitor their
platforms, like the provisions recommended for
algorithmic trading in the Regulatory and
Implementing Standards related to MiFID II.
• From a regulatory perspective, cybersecurity
continues to be a key focus area, both in the
United States and around the world. The Office of
the Comptroller of the Currency (OCC) identified
cybersecurity and operational resiliency as a
priority in its 2020 bank supervision programs,
with particular emphasis on threat vulnerability
and detection, access controls and data
management, and managing
third-party connections.
Robo-advisors should continue evolving their
approach to keep up with the myriad rules and
regulations that they are facing, to take advantage
of the untapped opportunity in wealth, asset
management and retirement markets.
Fig: Challenges for Robo Advisor
1.4 Challenges faced by Robo-Advisors
The robo-advisory is still far from mainstream
In 2016, robo-advisor assets under management
was forecasted to reach $2.2 trillion worldwide by
2020 and $4.1 trillion by 2022. But currently, as of
2020, AUM for robo-advisory is around $1.4 trillion
with only a select few standalone robo-advisors like
Betterment and Wealthfront gaining scale. Unlike
Uber in personal transportation and Robinhood,
closer home in online brokerage, robo-advice has
not lived up to the visionary outlook to make advice
accessible to all and revolutionize
personal investing.
Even as passive investing is becoming popular
(there is over $6.3 trillion invested in global
ETF/ETPs at the end of December 31, 2019 up
from around $3.0 trillion at the end of 2015, AUM
for robo-advisors has not followed the same growth
pattern, though most robo-generated portfolios
leverage ETFs.
Robo-advisors will need advanced technologies to
improve their algorithms, drive more
personalization in their offerings for millennials/
Gen Z investors. They need to involve human
advisory at higher portfolio thresholds and expand
distribution through Robo-for-advisor solutions. In
addition, Robo-advisors can cross-sell and upsell
other products like retirement and CDs, and
differentiate based on the customer’s risk appetite
and investment preferences.
Standalone Robo-Advisors Are Struggling To Break Even
Per experts, robo-advisors should manage between
$11.3 billion and $21.5 billion to break even. In
Europe, the breakeven AUM falls to $3.5 billion to
$5.3 billion, based on a higher fee level of 0.45%.
The main reasons robo-advisors struggle to break
even are the low service fees, low average portfolio
size, and high marketing costs required to acquire
customers. For some customers, the revenue can be
as low as $100 per year, whereas customer
acquisition costs (CAC) for most robo-advisors are
from $300 to $1,000 per client.
As income and responsibilities increase, financial
planning gets complex and requires constant
intervention that can be provided by a human
financial advisor only. The early adopters of
robo-advisors tend to move to traditional advisory
with an increase in their wealth. There is a need for
standalone robo-advisors to tie up with incumbents
and specialized fintechs to fulfill the needs of this
migratory segment as they move to the higher end
of the mass affluent or enter the affluent segment.
Getting tested during uncertain and highly volatile bearish market (e.g. COVID-19)
COVID-19 has wreaked havoc on investor portfolios.
Robo-portfolios are no exception, given their
dependence on ETFs, which have seen a sharp price
correction. Ensuing market conditions will test
investment and business models of robo-advisors
as this segment will be facing its first
economic downturn.
The market reaction is different for different firms.
For Wealthfront, new investment account signups
were “through the roof ” during the market volatility
while Betterment saw more “dip buying”, but
overall, Betterment saw more inflows than
outflows, and most inbound calls were about
tax-loss harvesting and how to do more of it.
Charles Schwab’s Intelligent Portfolios captured
more of the market’s year-to-date downside than
upside, mainly attributed to Schwab’s
higher-than-average exposure to international
equities, small exposure to high-yield and
international fixed income, and high
cash allocation.
Robo-advisory firms, by design, have advantages in
the crisis due to a focus on goal-based investing,
which helps align the investment risk reward based
on the risk appetite and time horizon of the
investors. However, there are several aspects where
robo-advice can improve in being responsive during
such large market disruptions -
• Leading robo-advisory platforms need to show
that their solutions can reliably manage downside
risk and adjust investment llocationsaccordingly,
per the investor’s goals and objectives, while
taking into consideration long-term investing &
short-term economic reactions.
• Integrated tools for chat functionality or video
conference to connect with a team of human
advisors for clarifications on robo-advised
portfolios and market-related
adjustments needed
• Monetize technology platforms to drive higher
revenues through licensing and white-label
services and life-planning functionalities to
prepare investors for adverse market scenarios.
For example, investors can take advantage of
scenario planning tools that model impact to
investment goals and provide a view on financial
measures to help survive a job loss/ income
contraction during the crisis.
Standalone Robo-Advisors Are Struggling To Break Even
Still far from mainstream
Getting tested during uncertain and highly volatile bearish market
Challenges
Getting tested during uncertain and highly volatile bearish market (e.g. COVID-19)
COVID-19 has wreaked havoc on investor portfolios.
Robo-portfolios are no exception, given their
dependence on ETFs, which have seen a sharp price
correction. Ensuing market conditions will test
investment and business models of robo-advisors
as this segment will be facing its first
economic downturn.
The market reaction is different for different firms.
For Wealthfront, new investment account signups
were “through the roof ” during the market volatility
while Betterment saw more “dip buying”, but
overall, Betterment saw more inflows than
outflows, and most inbound calls were about
tax-loss harvesting and how to do more of it.
Charles Schwab’s Intelligent Portfolios captured
more of the market’s year-to-date downside than
upside, mainly attributed to Schwab’s
higher-than-average exposure to international
equities, small exposure to high-yield and
international fixed income, and high
cash allocation.
Robo-advisory firms, by design, have advantages in
the crisis due to a focus on goal-based investing,
which helps align the investment risk reward based
on the risk appetite and time horizon of the
investors. However, there are several aspects where
robo-advice can improve in being responsive during
such large market disruptions -
• Leading robo-advisory platforms need to show
that their solutions can reliably manage downside
risk and adjust investment llocationsaccordingly,
per the investor’s goals and objectives, while
taking into consideration long-term investing &
short-term economic reactions.
• Integrated tools for chat functionality or video
conference to connect with a team of human
advisors for clarifications on robo-advised
portfolios and market-related
adjustments needed
• Monetize technology platforms to drive higher
revenues through licensing and white-label
services and life-planning functionalities to
prepare investors for adverse market scenarios.
For example, investors can take advantage of
scenario planning tools that model impact to
investment goals and provide a view on financial
measures to help survive a job loss/ income
contraction during the crisis.
Perspective: Future Scope of Robo-Advisory
2.1 Hybrid Robo-Advisors
Hybrid models combining the best of both worlds
(digital and traditional) are seen as the future of
investing with digital solutions for investment
experience, digital-led advisor connects, and
robo-models for goal-based investments with
added options of human advisor interactions.
Some of the advantages of the hybrid
model are as follows:
• Access to a broader client pool, market segments
and new revenue streams through digital first,
real-time, low-cost access to advice
• Focus on understanding the more complex
financial planning needs of the customer while
the core investment portfolio and other middle
and back-office processes can be addressed by
the robo-advisory solutions
• Integrate human advisor insights and inputs into
the investment process to assist with portfolio
rebalancing decisions
• Pricing for the digital advisory could be
modularized for value-added services like tax loss
harvesting, new investment products, and
portfolio review by registered advisors
2.2 Role of technology across various touchpoints in enhancement of robo-advisory
The below figure shows the various touchpoints of
robo-advisory across the wealth management value
chain with future potential for technological
improvement.
With the advancement of technology in areas such
as advanced analytics, artificial intelligence
(machine learning) and natural language
processing, the effectiveness of robo-advisory is
set to increase. This will enable robo-advisors to
have higher impact across the value chain further
strengthening the value proposition.
Category TasksDivision
Marketing & client acquisition
Strategy development
Client Onboarding & administration
Client Due Diligence/ KYC
Pre-Trade Analytics
Measure results
Risk Profiling
Financial Planning
Tax Planning
Analytics and visualization
Cash Flow Assessment
Trade routing and processing
Commission and fee allocation
Business Audits
Data Security
Data warehousing & BI
Investment Planning
Goal Setting
Investment Research
Portfolio Construction
Performance and Trade Reporting
Pre-trade compliance
Regulatory Monitoring
Data acquisition & provisioning
Research, Analytics and
Allocation
Portfolio Management
Risk & reporting
Transactions/trade execution
Accounting Billing
Allocation
Rebalancing
Reconciliation
Education
Legal & Compliance
Data management
Account Opening/ Closing/
Maintenance
Lead generation, prospecting,
referrals
Customer management/
Client Relations
Portfolio Monitoring and
Alerts
Ad-hoc and client self-service
reporting
Risk Monitoring and
Reporting
Trade matching and
settlement
Trade and Portfolio
Accounting
Mid
dle
Offi
ceFr
ont
Offi
ceB
ack
Offi
ce
Current State
Future State
No impact
Low Impact
Low impact
Medium Impact
Medium Impact
High Impact
High Impact
Very High Impact
Marketing and Client Acquisition
The cost of acquisition for mass affluent customers
is a big challenge for robo-advisory firms. New-age
marketing tools such as content marketing can be
used to create marketing material in different
formats like videos, case studies, articles, and
podcasts. By leveraging social media analytics to
understand customer preferences, the suitable
format can be used to reach out to the customer.
Website and clickstream analytics will be helpful to
drive more conversions and ensure the
effectiveness of each marketing program. Further,
programmatic marketing solutions can be used to
optimize the marketing spend and drive
cost-effective acquisitions.
Client onboarding and administration:
• Account Opening: The account opening can be
fully digitized in the future through video
conferencing, digital signatures, biometric
authentication, and online ID verification. It can
reduce the time taken to open a new account to
few minutes and lead to significant cost-savings.
• KYC/Due Diligence and Risk Profiling: In KYC
information, additional data points like expense
profile, behavioral data, investment history, and
credit history can be sourced from external
partners. Based on the gathered information,
customer’s investment experience, liquidity
needs, and risk appetite can be understood, and a
robo-advisor can leverage analytical models to
generate risk alerts and overall investment
guidance for the profile. This can be available
from Day One of onboarding, thereby improving
customer satisfaction.
• Customer Management/ Client Relations: NLP
and sentiment analysis can be leveraged to
analyze investor queries during different market
conditions to make profile updates, enabling an
empathetic approach for robo-advisors.
Investment Planning:
• Goal Setting: Robo-advisor can proactively make
portfolio suggestions based on market trends and
peer strategies among similar investment
personas. The robo-advisors can also be used to
cross-sell products like credit extension
(considering credit score) if there is a mismatch
between the goals and financial status of the
customer, and provide a cost-benefit analysis for
better decision making.
• Education: Robo-advisory can drive investor
education with gamification and online learning
modules. This will ultimately help in deeper
insights, enhancing the portfolio management,
relationship management, cross-selling, sales,
and new product development.
• Investment Planning: Robo-advisors can use big
data to create an optimal roadmap for achieving
the goals of the investor. Big data analytics can be
leveraged to model redemption needs linked to
life events, improve personalization through
360-degree customer views, and identify
appropriate investment products to meet
financial goals.
• Tax planning: Tools to optimize investment
returns through effective tax planning and tax
loss harvesting are integral to wealth and
investment management. Robo-advisors use tax
loss harvesting algorithms to strategically
balance the capital gains with capital losses to
maximize tax savings by complying with the laws.
Robo-advisors can build stronger automation to
help with optimized portfolio strategies for
tax reduction.
Research, Analytics, and Allocation:
• Research and Analytics: Data mining and NLP can
be used for investment research and making
portfolio changes to drive better returns. The NLP
engine can do multiple weeks’ worth of forensic
accounting, analyzing corporate profits,
valuations, and current stock prices in hours to
deliver investment signals to support
active-investing strategies akin to that of large
wealth managers. Data mining can also be helpful
in exploring alternative investment options and
understanding international markets, overcoming
the home country bias. Identifying right growth
opportunities in emerging markets can also lead
to much better returns.
• Allocation: The process of allocation is automated
by most robo-advisory companies, taking away
the risk of irrational decision-making by the
customer during market disruptions. Based on the
customer’s risk profile and return expectation, the
selection of suitable asset class is done. The
algorithm should be able to identify the
correlation among different asset classes to
minimize the market risks using regression
analysis and maximizing diversification benefits.
Portfolio Management:
• Portfolio Construction: Robo-advisory models can
be implemented for new investment products
such as retirement and real-estate investing
(which are currently not mainstream among
robo-advisors). Robo-advisor platforms can be
further enhanced for advice on non-investment
financial products like mortgages.
• Portfolio Monitoring and Alerts: Robo-advisors
can adopt emerging technologies such as hybrid
neuro-fuzzy systems including Adaptive NFIS,
Gaussian RBF, and neural based Q-learning for
understanding the dynamic non-linear nature of
financial markets and the unstructured nature of
the investment decision-making process in
several asset classes. They can create alerts to
change the portfolio in advance to counter the
potential downside.
• Rebalancing: Rebalancing is counterintuitive to
humans as they have the tendency to increase
investment in over-performing assets.
Robo-advisors automatically rebalance the
portfolio if some assets are over- or
underperforming, ensuring long-term gains.
However, extreme market fluctuations can trigger
inappropriate portfolio rebalancing. Using
self-learning algorithms and adaptive models,
robo-advisors can continuously improve their
algorithms to intelligently redistribute the profits
and tax-savings to maximize the returns for
the customer.
Risk & Reporting:
• Big data in combination with analytics helps in
portfolio monitoring and risk scoring, with
automated or push-based actions for portfolio
changes and decisions
• Solutions with built-in analytics and reporting
tools allow administrators to monitor portfolio
and individual investor level risks. BlackRock's
Aladdin, for example, provides early warnings on
risk factors with individualized risk reports that
can soon become mainstream in robo-driven
advice solutions
Accounting:
• RPA is used to run queries, perform calculations
and data validation checks to support different
components of the tax assessment and filing
process. Fund expense processes can also
leverage RPA technology to run validation checks,
process payments based on pre-defined criteria
and budget forecasts. The adoption of new
technologies in accounting is low but can
increase in the future to augment the
human workforce.
Transactions/Trade Execution:
• Pre-trade Compliance: Robo-advisor algorithms
can automatically factor in the trade compliance
needs, by integrating with a compliance engine
that continuously tracks the regulatory changes
and compliance requirements at every level
• Trade operation: Complex AI systems are used to
make extremely fast trading decisions. Millions of
transactions are to be done per day by
robo-advisors incorporating high-frequency
trading (HFT) algorithms. The adoption of machine
learning and deep learning algorithms for
calibrating trading decisions in real time will
increase in the coming years if robo-advisors are
able to scale up their customer base.
Legal & Compliance:
• The role of technology for legal and compliance
purposes is currently in its nascent stage.
Regulatory technology (RegTech) solutions will
become a ‘must-have’ for robo-advisory firms to
comply with regulatory pressure and real-time
monitoring. RPA can be used to automatically
generate disclosure reports for market regulators.
Machine learning can be used to improve flagging
accuracy by creating numerous specific
use-cases to understand the trading patterns and
capital inflow/outflow of the customer.
• With further evolvement of RegTech in the asset
and wealth management industry, the compliance
team can be better utilized to deal only with
strategic overrides and managing exceptions.
Data management:
• The huge amounts of qualitative and quantitative
data extracted from different sources in the
middle office, trading and accounting, and back
office operations is saved in the data warehouses.
The different aspects of data management can be
used for building algorithms to effectively run
constituent processes in the wealth management
value chain. With the help of ETL tools, insight
generation and effectiveness of robo-advisors
can be improved significantly. Data visualization
tools can be helpful in creating engaging
dashboards and trade reports for the customers
and financial advisors, enabling effective, s
trategic decision-making.
Private banking wealth
Managers
Hybrid Players
HNI / UHNI
Affluent
Mass affluent / Retail
Standalone robo-
advisors
Ideal customer base for each type of player on the basis of
typical offerings
Clie
nt’s
exp
ecta
tion
/ aff
orda
bilit
y of
pe
rson
al a
dvic
e
Complexity of advisory needs Complexity of advisory needs
Private banking wealth
Managers
Hybrid Players
HNI / UHNI
Affluent
Mass affluent / Retail
Standalone robo-
advisors
Expansion of customer base led by technological expansion
Clie
nt’s
exp
ecta
tion
/ aff
orda
bilit
y of
pe
rson
al a
dvic
e
Standalone players
With improved robo-advisory algorithms and better
personalization, robo-advisors can evolve to
address the needs of the affluent segment.
Smoother customer onboarding, improved
reporting, and premium offerings incorporating
human advisory connects and chatbots can drive
higher adoption.
Hybrid players
Existing hybrid advisory players can leverage
technology for digitization of various middle office
and back-end operations and leverage financial
advisor networks to address client needs. Financial
advisors can be judiciously used based on the
segment they are dealing with. More advanced
robo-advice models for direct stock investments,
community/ peer-based investment models, and
alternative investment options like real estate and
commodity investments can be added to increase
the value delivery at the higher end of the market
Traditional players need to digitize and modernize
extensively to offer Robo / Digital advice driven
models. Integration with existing Fintechs can be
used to accelerate the go to market. New Digital
advisory offerings will need to be incubated through
innovation centers and partnerships with large
technology firms to ensure that client and user
experience delivered are best-in-class and in line
with industry-leading digital offerings. The
advantage for the incumbents will be a potential
captive market for digital advice with the ability to
attract underpenetrated affluent and mass affluent
segments. As millennials and Gen Z customers
build assets, the opportunity will be to help them
with a larger suite of financial needs including
retirement, mortgages, and dedicated wealth
advisory. Standalone players will need to be agile to
maximize revenue opportunities by building up
distribution and scaling up their
technology platforms.
2.3 Way Forward
By embracing technological advancements, wealth
management firms in the human to robo-advisory
continuum will be looking to expand their services
by providing enhanced customer experience,
personalization, and better advisory outcomes.
Traditional wealth managers and standalone
robo-advisors can expand market share by taking
the hybrid approach while existing hybrid players
can expand at both ends of the spectrum:
More than a decade since their inception, robo-advisors are at an
inflection point. Despite the buzz and early success, mainstream
adoption has been elusive. They are now facing the first major test
for their goal-based, algorithm-driven investment model; the
COVID-19 crisis has brought in an unprecedented level of uncertainty
for investors across all categories. The ultimate impact on investor
wealth and how well robo-advisors can wade through this crisis
remains to be seen.
Incumbents have adopted robo-advisory models to launch their own
offerings quickly gaining scale by leveraging their existing customer
base. The future may well be all digital but traditional advice will
continue to hold sway at the top end of the market. Traditional
wealth managers and independent advisors will increasingly use
robo-for-advice models to serve the high end of the market. The
standalone robo-advisors are at a juncture where they have to decide
if they want to expand by innovating on product offerings and building
market partnerships or get sold out to larger firms to help bridge the
gaps in the market.
References
• “BCG-Reigniting-Radical-Growth-June-2019”
• “BCG Global Digital Wealth Management Report 2019-2020”
• “AITE-US-Digital-Investment-Management-
Market-Monitor-Q2-2020-Report”
https://www.statista.com/outlook/337/100/robo-advisors/worldwide
Source:
ETFGI for ETF industry data
Conclusion
Pradeep Agarwal
Wipro Insights Team, Securities and Capital Markets
Pradeep is the Wipro Insights lead for the Securities and Capital Markets practice at Wipro. He has over 12
years of experience in the Financial Services sector having worked with EY, HSBC Investment Banking and
Guggenheim Transparent Value. Pradeep is skilled at performing sector research, forming go-to-market
strategies, capital advisory, M&A origination and execution. You can reach him at
Shubham Kapoor
Solution Design Consultant, Securities and Capital Markets
Shubham works on developing relevant solutions/response designs for technology proposals in the Capital
Markets industry. He is also tasked with carrying out research activities for ongoing deals and devising
strategy plans for the vertical. He has completed his management degree from IIM Udaipur with a
specialization in the area of strategy and finance, and is well versed in matters related to the Capital Markets
industry. He can be reached at [email protected]
Sakshi Agarwal
Solution Design Consultant, Securities and Capital Markets
Sakshi works as a consultant for developing IT solutions/response designs for customer proposals in the
Capital Markets industry. She is also responsible for bid management and devising strategy for lead
generation. Sakshi holds a management degree from IIM Udaipur with a specialization in the area of finance
and strategy and has proven expertise in the Capital Markets industry. She is CFA level 1 certified by CFA
Institute. She can be reached at [email protected]
Swarnabha Seth
Solution Design Lead, Securities and Capital Markets
Swarnabha is a solution design lead in the Security and Capital markets division at Wipro. He has more than
14 years of experience across solution development, consulting, and sales strategy. He also has a deep
technology understanding of the digital space and has been instrumental in shaping large deals for banking,
insurance and financial services domains. You can reach him at [email protected]
About authors
Wipro LimitedDoddakannelli,Sarjapur Road,Bangalore-560 035,IndiaTel: +91 (80) 2844 0011Fax: +91 (80) 2844 0256wipro.com
Wipro Limited (NYSE: WIT, BSE: 507685, NSE: WIPRO) is a leading global information technology, consulting and business process services company. We harness the power of cognitive computing, hyper-automation, robotics, cloud, analytics and emerging technologies to help our clients adapt to the digital world and make them successful. A company recognized globally for its comprehensive portfolio of services,
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IND/TBS/MAY-DEC 2020